Identification of tumor suppressors and oncogenes from genomic and epigenetic features in ovarian cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.     

MSIsensor-pro: Fast,Accurate, and Matched-normal-sample-free Detection of Microsatellite Instability

Microsatellite instability(MSI) is a key biomarker for cancer therapy and prognosis. Traditional experimental assays are laborious and time-consuming, and next-generation sequencingbased computational methods do not work on leukemia samples, paraffin-embedded samples, or patient-derived xenografts/organoids, due to the requirement of matched normal samples. Herein,we developed MSIsensor-pro, an open-source single sample MSI scoring method for research and clinical applications. MSIsensor-pro introduces a multinomial distribution model to quantify polymerase slippages for each tumor sample and a discriminative site selection method to enable MSI detection without matched normal samples. We demonstrate that MSIsensor-pro is an ultrafast,accurate, and robust MSI calling method. Using samples with various sequencing depths and tumor purities, MSIsensor-pro significantly outperformed the current leading methods in both accuracy and computational cost. MSIsensor-pro is available at https://github.com/xjtu-omics/msisensor-pro and free for non-commercial use, while a commercial license is provided upon request.     

SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.     

Expression changes in the stroma of prostate cancer predict subsequent relapse

Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.     

Evaluating somatic tumor mutation detection without matched normal samples

Background

Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual’s inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a “normal” sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.

Results

We evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43–60%) but lesser precision (20–21%) to current matched tumor/normal approaches (recall 43–73%, precision 30–82%) when compared to a “gold-standard” tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.

Conclusions

A detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

     

Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples.     

Tumor-infiltrating lymphocytes in colorectal tumors display a diversity of T cell receptor sequences that differ from the T cells in adjacent mucosal tissue

Tumors from colorectal cancer (CRC) are generally immunogenic and commonly infiltrated with T lymphocytes. However, the details of the adaptive immune reaction to these tumors are poorly understood. We have accrued both colon tumor samples and adjacent healthy mucosal samples from 15 CRC patients to study lymphocytes infiltrating these tissues. We apply a method for detailed sequencing of T-cell receptor (TCR) sequences from tumor-infiltrating lymphocytes (TILs) in CRC tumors at high throughput to probe T-cell clones in comparison with the TCRs from adjacent healthy mucosal tissue. In parallel, we captured TIL counts using standard immunohistochemistry. The variation in diversity of the TIL repertoire was far wider than the variation of T-cell clones in the healthy mucosa, and the oligoclonality was higher on average in the tumors. However, the diversity of the T-cell repertoire in both CRC tumors and healthy mucosa was on average 100-fold lower than in peripheral blood. Using the TCR sequences to identify and track clones between mucosal and tumor samples, we determined that the immune response in the tumor is different than in the adjacent mucosal tissue, and the number of shared clones is not dependent on distance between the samples. Together, these data imply that CRC tumors induce a specific adaptive immune response, but that this response differs widely in strength and breadth between patients.     

A new method of cryopreserving colorectal carcinoma cells for patient derived xenograft model generation

Patient derived xenograft (PDX) models provide an efficient way to study anti-tumor drug efficacy. In this respect, it is essential to study the optimal method needed to cryopreserve the starting cells obtained from tumor samples for PDX model generation. Cryopreservation of cells prior to xenografting is necessary for cross-verification of results obtained by xenografting and also for practical planning of experiments. In the present work, we studied the cryopreservation of colorectal carcinoma (CRC) cells isolated from patient tumor samples for generating their patient derived xenograft models. CRC therapeutics study is essential for early stage intervention and treatment of the disease. CRC cell lines do not ideally depict the molecular characteristics of patient CRC tumor samples. This necessitates the generation of CRC PDX models for drug discovery. We show that CRC cells isolated from patient tumor samples have comparable recovery, viability and growth with both conventional cryopreservation methods as well as Fibulas BioFlash Drive™. However, xenograft tumor formation was much more effective with Fibulas BioFlash Drive™ cryopreserved cells than with cells cryopreserved with conventional methods. Therefore, we put forward an effective way to cryopreserve primary cells obtained from patient tumor samples for PDX model generation in this study.     

scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence

Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepato-cellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chro-mosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome hetero-geneity and evolution.     

Genome-Wide Identification of Somatic Aberrations from Paired Normal-Tumor Samples

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.     

Validation of fluorescent SSCP analysis for sensitive detection of p53 mutations

We have developed a fluorescence-based single strand conformation polymorphism (SSCP) method that offers fast and sensitive screening for mutations in exons 5-8 of the human p53 gene. The method uses an ABI 377 DNA sequencer for unique color detection of each strand, plus accurate alignment of lanes for better detection of mobility shifts. To validate the method, 21 cell lines with reported mutations in p53 exons 5-8 were analyzed by SSCP using various gel conditions. The sensitivity for mutation detection was 95% for all cell lines studied, and no false positives were seen in 10 normal DNA samples for all four exons. Experiments mixing known amounts of tumor and normal DNA showed that mutations were detected even when tumor DNA was mixed with 80% normal DNA. Fluorescent SSCP analysis using the ABI sequencer is a useful tool in cancer research, where screening large numbers of samples for p53 mutations is desired.     

RNA mutations of prox1 detected in human esophageal cancer cells by the shifted termination assay

We have recently reported a novel finding that a candidate tumor suppressor gene prox1 suffered adenosine-to-inosine (A-to-I) RNA mutation without genomic mutation in a subset of human cancer cells and lost its function. Hence, screening of mutations in both cDNA and genomic DNA could be important in the analysis of causes for cancers. Here, we applied a sensitive, accurate, and simple method, called shifted termination assay (STA) for detection of an A-to-I RNA mutation (R334G) in prox1. We prepared PCR-amplified samples containing the target base of RNA mutation from cDNAs and genomic DNAs of various cell lines and clinical samples, to demonstrate that the STA method can be used to identify not only genomic mutations but also RNA mutations more effectively compared to sequencing. By means of STA, we found prox1 R334G RNA mutations but not genomic DNA mutations in 4 of 8 cases of esophageal cancers. This method can help us to detect RNA mutation effectively and progress research of a potential oncogenic principle.     

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov     

RT-PCR method to quantify vascular endothelial growth factor expression.

The vascular endothelial growth factor (VEGF) is implicated in the progression of cancers. Its expression is well correlated with tumor growth and metastases. The availability of a rapid and sensitive method to detect the amounts of VEGF mRNA in biological samples of limited size, very small biopsies, or samples containing relatively few cells could provide an interesting prognostic tool for clinicians. We have developed an RT-PCR method that allows us to detect the VEGF mRNA from as little as 3 micrograms total mRNA. We have also shown that this protocol can be generalized to all cell lines tested. This method constitutes a very potent tool for the analysis of VEGF mRNA expression in different contexts.     

A mathematical model for the determination of steady-state cardiolipin remodeling mechanisms using lipidomic data

Technical advances in lipidomic analysis have generated tremendous amounts of quantitative lipid molecular species data, whose value has not been fully explored. We describe a novel computational method to infer mechanisms of de novo lipid synthesis and remodeling from lipidomic data. We focus on the mitochondrial-specific lipid cardiolipin (CL), a polyglycerol phospholipid with four acyl chains. The lengths and degree of unsaturation of these acyl chains vary across CL molecules, and regulation of these differences is important for mitochondrial energy metabolism. We developed a novel mathematical approach to determine mechanisms controlling the steady-state distribution of acyl chain combinations in CL . We analyzed mitochondrial lipids from 18 types of steady-state samples, each with at least 3 replicates, from mouse brain, heart, lung, liver, tumor cells, and tumors grown in vitro. Using a mathematical model for the CL remodeling mechanisms and a maximum likelihood approach to infer parameters, we found that for most samples the four chain positions have an independent and identical distribution, indicating they are remodeled by the same processes. Furthermore, for most brain samples and liver, the distribution of acyl chains is well-fit by a simple linear combination of the pools of acyl chains in phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG). This suggests that headgroup chemistry is the key determinant of acyl donation into CL, with chain length/saturation less important. This canonical remodeling behavior appears damaged in some tumor samples, which display a consistent excess of CL molecules having particular masses. For heart and lung, the "proportional incorporation" assumption is not adequate to explain the CL distribution, suggesting additional acyl CoA-dependent remodeling that is chain-type specific. Our findings indicate that CL remodeling processes can be described by a small set of quantitative relationships, and that bioinformatic approaches can help determine these processes from high-throughput lipidomic data.